{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd   \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.callbacks import * \n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "from kerashypetune import KerasGridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8760, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Ora solare</th>\n",
       "      <th>Livello P.Salute Canal Grande (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>01-gen-09</td>\n",
       "      <td>01:00</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>01-gen-09</td>\n",
       "      <td>02:00</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>01-gen-09</td>\n",
       "      <td>03:00</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>01-gen-09</td>\n",
       "      <td>04:00</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>01-gen-09</td>\n",
       "      <td>05:00</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Data Ora solare  Livello P.Salute Canal Grande (cm)\n",
       "0  01-gen-09      01:00                                34.0\n",
       "1  01-gen-09      02:00                                37.0\n",
       "2  01-gen-09      03:00                                36.0\n",
       "3  01-gen-09      04:00                                29.0\n",
       "4  01-gen-09      05:00                                20.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### READ DATA ###\n",
    "\n",
    "df = pd.read_csv('Punta_Salute_2009.csv', sep=';')\n",
    "df = df.dropna()\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235b2000438>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT WEEKLY TREND ###\n",
    "\n",
    "df[:7*24]['Livello P.Salute Canal Grande (cm)'].plot(\n",
    "    y='Livello P.Salute Canal Grande (cm)', x='Ora solare', figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE T2V LAYER ###\n",
    "\n",
    "class T2V(Layer):\n",
    "    \n",
    "    def __init__(self, output_dim=None, **kwargs):\n",
    "        self.output_dim = output_dim\n",
    "        super(T2V, self).__init__(**kwargs)\n",
    "        \n",
    "    def build(self, input_shape):\n",
    "\n",
    "        self.W = self.add_weight(name='W',\n",
    "                                shape=(input_shape[-1], self.output_dim),\n",
    "                                initializer='uniform',\n",
    "                                trainable=True)\n",
    "\n",
    "        self.P = self.add_weight(name='P',\n",
    "                                shape=(input_shape[1], self.output_dim),\n",
    "                                initializer='uniform',\n",
    "                                trainable=True)\n",
    "\n",
    "        self.w = self.add_weight(name='w',\n",
    "                                shape=(input_shape[1], 1),\n",
    "                                initializer='uniform',\n",
    "                                trainable=True)\n",
    "\n",
    "        self.p = self.add_weight(name='p',\n",
    "                                shape=(input_shape[1], 1),\n",
    "                                initializer='uniform',\n",
    "                                trainable=True)\n",
    "\n",
    "        super(T2V, self).build(input_shape)\n",
    "        \n",
    "    def call(self, x):\n",
    "        \n",
    "        original = self.w * x + self.p\n",
    "        sin_trans = K.sin(K.dot(x, self.W) + self.P)\n",
    "        \n",
    "        return K.concatenate([sin_trans, original], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CREATE GENERATOR FOR LSTM AND T2V ###\n",
    "\n",
    "sequence_length = 24\n",
    "\n",
    "def gen_sequence(id_df, seq_length, seq_cols):\n",
    "    \n",
    "    data_matrix = id_df[seq_cols].values\n",
    "    num_elements = data_matrix.shape[0]\n",
    "\n",
    "    for start, stop in zip(range(0, num_elements-seq_length), range(seq_length, num_elements)):\n",
    "        yield data_matrix[start:stop, :]\n",
    "\n",
    "def gen_labels(id_df, seq_length, label):\n",
    "    \n",
    "    data_matrix = id_df[label].values\n",
    "    num_elements = data_matrix.shape[0]\n",
    "    \n",
    "    return data_matrix[seq_length:num_elements, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE MODEL STRUCTURES ###\n",
    "\n",
    "def set_seed(seed):\n",
    "    \n",
    "    tf.random.set_seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    \n",
    "\n",
    "def T2V_NN(param, dim):\n",
    "    \n",
    "    set_seed(33)\n",
    "    \n",
    "    inp = Input(shape=(dim,1))\n",
    "    x = T2V(param['t2v_dim'])(inp)\n",
    "    x = LSTM(param['unit'], activation=param['act'])(x)\n",
    "    x = Dense(1)(x)\n",
    "    \n",
    "    m = Model(inp, x)\n",
    "    m.compile(loss='mse', optimizer=Adam(lr=param['lr']))\n",
    "    \n",
    "    return m\n",
    "\n",
    "\n",
    "def NN(param, dim):\n",
    "    \n",
    "    set_seed(33)\n",
    "    \n",
    "    inp = Input(shape=(dim,1))\n",
    "    x = LSTM(param['unit'], activation=param['act'])(inp)\n",
    "    x = Dense(1)(x)\n",
    "    \n",
    "    m = Model(inp, x)\n",
    "    m.compile(loss='mse', optimizer=Adam(lr=param['lr']))\n",
    "    \n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### PREPARE DATA TO FEED MODELS ###\n",
    "\n",
    "X, Y = [], []\n",
    "for sequence in gen_sequence(df, sequence_length, ['Livello P.Salute Canal Grande (cm)']):\n",
    "    X.append(sequence)\n",
    "    \n",
    "for sequence in gen_labels(df, sequence_length, ['Livello P.Salute Canal Grande (cm)']):\n",
    "    Y.append(sequence)\n",
    "    \n",
    "X = np.asarray(X)\n",
    "Y = np.asarray(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6132, 24, 1) (6132, 1)\n",
      "(2604, 24, 1) (2604, 1)\n"
     ]
    }
   ],
   "source": [
    "### TRAIN TEST SPLIT ###\n",
    "\n",
    "train_dim = int(0.7*len(df))\n",
    "X_train, X_test = X[:train_dim], X[train_dim:]\n",
    "y_train, y_test = Y[:train_dim], Y[train_dim:]\n",
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE PARAM GRID FOR HYPERPARM OPTIMIZATION ###\n",
    "\n",
    "param_grid = {\n",
    "    'unit': [64,32],\n",
    "    't2v_dim': [128,64],\n",
    "    'lr': [1e-2,1e-3], \n",
    "    'act': ['elu','relu'], \n",
    "    'epochs': 200,\n",
    "    'batch_size': [512,1024]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "32 trials detected for ('unit', 't2v_dim', 'lr', 'act', 'epochs', 'batch_size')\n",
      "\n",
      "***** (1/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 3.93971 at epoch 18\n",
      "\n",
      "***** (2/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 20.94701 at epoch 24\n",
      "\n",
      "***** (3/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 4.54556 at epoch 38\n",
      "\n",
      "***** (4/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 8.50502 at epoch 28\n",
      "\n",
      "***** (5/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 3.6678 at epoch 63\n",
      "\n",
      "***** (6/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 4.41179 at epoch 78\n",
      "\n",
      "***** (7/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 5.4565 at epoch 42\n",
      "\n",
      "***** (8/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 128, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 9.25311 at epoch 28\n",
      "\n",
      "***** (9/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 7.76763 at epoch 23\n",
      "\n",
      "***** (10/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 19.17314 at epoch 58\n",
      "\n",
      "***** (11/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 18.60823 at epoch 29\n",
      "\n",
      "***** (12/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 8.6984 at epoch 70\n",
      "\n",
      "***** (13/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 4.02575 at epoch 40\n",
      "\n",
      "***** (14/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 4.04234 at epoch 60\n",
      "\n",
      "***** (15/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 5.85961 at epoch 37\n",
      "\n",
      "***** (16/32) *****\n",
      "Search({'unit': 64, 't2v_dim': 64, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 10.29532 at epoch 22\n",
      "\n",
      "***** (17/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 7.46377 at epoch 8\n",
      "\n",
      "***** (18/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 3.66566 at epoch 30\n",
      "\n",
      "***** (19/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 4.32677 at epoch 20\n",
      "\n",
      "***** (20/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 3.74955 at epoch 40\n",
      "\n",
      "***** (21/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 5.75946 at epoch 24\n",
      "\n",
      "***** (22/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 5.73273 at epoch 39\n",
      "\n",
      "***** (23/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 7.12696 at epoch 20\n",
      "\n",
      "***** (24/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 128, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 4.61313 at epoch 52\n",
      "\n",
      "***** (25/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 5.17263 at epoch 16\n",
      "\n",
      "***** (26/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 5.48323 at epoch 14\n",
      "\n",
      "***** (27/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 4.26736 at epoch 37\n",
      "\n",
      "***** (28/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 5.45053 at epoch 26\n",
      "\n",
      "***** (29/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 4.17188 at epoch 25\n",
      "\n",
      "***** (30/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 3.79269 at epoch 57\n",
      "\n",
      "***** (31/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 5.9002 at epoch 23\n",
      "\n",
      "***** (32/32) *****\n",
      "Search({'unit': 32, 't2v_dim': 64, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 3.38646 at epoch 80\n"
     ]
    }
   ],
   "source": [
    "### FIT T2V + LSTM ###\n",
    "\n",
    "es = EarlyStopping(patience=5, verbose=0, min_delta=0.001, monitor='val_loss', mode='auto', restore_best_weights=True)\n",
    "\n",
    "hypermodel = lambda x: T2V_NN(param=x, dim=sequence_length)\n",
    "\n",
    "kgs_t2v = KerasGridSearch(hypermodel, param_grid, monitor='val_loss', greater_is_better=False, tuner_verbose=1)\n",
    "kgs_t2v.search(X_train, y_train, validation_split=0.2, callbacks=[es], shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6758114681986895"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_t2v = kgs_t2v.best_model.predict(X_test).ravel()\n",
    "mean_absolute_error(y_test.ravel(), pred_t2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x235b22e9a58>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### VISUALIZE TEST PREDICTIONS ###\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.plot(pred_t2v[:365], label='prediction')\n",
    "plt.plot(y_test.ravel()[:365], label='true')\n",
    "plt.title('T2V plus LSTM'); plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "16 trials detected for ('unit', 'lr', 'act', 'epochs', 'batch_size')\n",
      "\n",
      "***** (1/16) *****\n",
      "Search({'unit': 64, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 567.87518 at epoch 7\n",
      "\n",
      "***** (2/16) *****\n",
      "Search({'unit': 64, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 4.17391 at epoch 51\n",
      "\n",
      "***** (3/16) *****\n",
      "Search({'unit': 64, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 49.53372 at epoch 21\n",
      "\n",
      "***** (4/16) *****\n",
      "Search({'unit': 64, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 4.12449 at epoch 41\n",
      "\n",
      "***** (5/16) *****\n",
      "Search({'unit': 64, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 105.60397 at epoch 5\n",
      "\n",
      "***** (6/16) *****\n",
      "Search({'unit': 64, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 4.91124 at epoch 72\n",
      "\n",
      "***** (7/16) *****\n",
      "Search({'unit': 64, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 8.16518 at epoch 40\n",
      "\n",
      "***** (8/16) *****\n",
      "Search({'unit': 64, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 8.89526 at epoch 41\n",
      "\n",
      "***** (9/16) *****\n",
      "Search({'unit': 32, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 15.80652 at epoch 16\n",
      "\n",
      "***** (10/16) *****\n",
      "Search({'unit': 32, 'lr': 0.01, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 79.86925 at epoch 7\n",
      "\n",
      "***** (11/16) *****\n",
      "Search({'unit': 32, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 745.1416 at epoch 4\n",
      "\n",
      "***** (12/16) *****\n",
      "Search({'unit': 32, 'lr': 0.01, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 11.01057 at epoch 19\n",
      "\n",
      "***** (13/16) *****\n",
      "Search({'unit': 32, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 5.39146 at epoch 90\n",
      "\n",
      "***** (14/16) *****\n",
      "Search({'unit': 32, 'lr': 0.001, 'act': 'elu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 138.7328 at epoch 19\n",
      "\n",
      "***** (15/16) *****\n",
      "Search({'unit': 32, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 512})\n",
      "SCORE: 7.71455 at epoch 35\n",
      "\n",
      "***** (16/16) *****\n",
      "Search({'unit': 32, 'lr': 0.001, 'act': 'relu', 'epochs': 200, 'batch_size': 1024})\n",
      "SCORE: 6.71738 at epoch 54\n"
     ]
    }
   ],
   "source": [
    "### FIT SIMPLE LSTM ###\n",
    "\n",
    "del param_grid['t2v_dim']\n",
    "\n",
    "es = EarlyStopping(patience=5, verbose=0, min_delta=0.001, monitor='val_loss', mode='auto', restore_best_weights=True)\n",
    "\n",
    "hypermodel = lambda x: NN(param=x, dim=sequence_length)\n",
    "\n",
    "kgs = KerasGridSearch(hypermodel, param_grid, monitor='val_loss', greater_is_better=False, tuner_verbose=1)\n",
    "kgs.search(X_train, y_train, validation_split=0.2, callbacks=[es], shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0274327399120446"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_nn = kgs.best_model.predict(X_test).ravel()\n",
    "mean_absolute_error(y_test.ravel(), pred_nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x235ba626390>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### VISUALIZE TEST PREDICTIONS ###\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.plot(pred_nn[:365], label='prediction')\n",
    "plt.plot(y_test.ravel()[:365], label='true')\n",
    "plt.title('single LSTM'); plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
